The global skills and competency framework for the digital world

Machine learning MLNG

Developing systems that learn through experience and by the use of data.

Levels of responsibility for this skill

2 3 4 5 6

Updates for SFIA 9

  • There is an updated version of this skill for SFIA 9.
  • Theme(s) influencing the updates for this skill: Continued refinement for AI/ML Ops skills, Making SFIA easier to consume (enhance readability/guidance/descriptions), Making SFIA easier to consume (updates to skill name/skill description).
  • Readability improvements have been made to levels 2, 3, 4, 5, and 6.
  • You can move to SFIA 9 when you are ready - SFIA 8 skill descriptions will still be available to use.
  • Previous SFIA assessments or skills mapping are not impacted by this change.

Guidance notes

Activities may include — but are not limited to:

  • evaluating trained models for their performance, robustness and bias
  • selecting and using metrics to examine outcomes
  • diagnosing and resolving issues before and after deployment
  • anticipating the organisational implications of machine learning models regarding ethics, bias, privacy, and data protection
  • establishing traceability for the outcomes produced by machine learning systems.

Understanding the responsibility levels of this skill

Where lower levels are not defined...
  • Specific tasks and responsibilities are not defined because the skill requires a higher level of autonomy, influence, and complexity in decision-making than is typically expected at these levels. You can use the essence statements to understand the generic responsibilities associated with these levels.
Where higher levels are not defined...
  • Responsibilities and accountabilities are not defined because these higher levels involve strategic leadership and broader organisational influence that goes beyond the scope of this specific skill. See the essence statements.

Developing skills and demonstrating responsibilities related to this skill

The defined levels show the incremental progression in skills and responsibilities.

Where lower levels are not defined...

You can develop your knowledge and support others who do have responsibility in this area by:

  • Learning key concepts and principles related to this skill and its impact on your role
  • Performing related skills (see the related SFIA skills)
  • Supporting others who are performing higher level tasks and activities
Where higher levels are not defined...
  • You can progress by developing related skills which are better suited to higher levels of organisational leadership.

Show/hide extra descriptions and levels.

Machine learning: Level 2

Level 2 - Assist: Essence of the level: Provides assistance to others, works under routine supervision, and uses their discretion to address routine problems. Actively learns through training and on-the-job experiences.

Applies given machine learning techniques to data, under the guidance of technical leadership.

Analyses and reports findings and remediates simple issues using algorithms implemented in standard software frameworks and tools.

Machine learning: Level 3

Level 3 - Apply: Essence of the level: Performs varied tasks, sometimes complex and non-routine, using standard methods and procedures. Works under general direction, exercises discretion, and manages own work within deadlines. Proactively enhances skills and impact in the workplace.

Applies existing machine learning techniques to new problems and datasets.

Evaluates the outcomes and performance of machine learning systems.

Identifies issues and recommends improvements to machine learning systems and the data they use.

Machine learning: Level 4

Level 4 - Enable: Essence of the level: Performs diverse complex activities, supports and guides others, delegates tasks when appropriate, works autonomously under general direction, and contributes expertise to deliver team objectives.

Given a well-described problem and dataset, assesses whether machine learning is likely to provide an effective solution.

Implements algorithms developed by others. Advises on the effectiveness of specific techniques, based on project findings and wider research.

Contributes to the development, evaluation, monitoring and deployment of machine learning systems.

Understands and applies rules and guidelines specific to the industry, and anticipates risks and other implications of modelling.

Machine learning: Level 5

Level 5 - Ensure, advise: Essence of the level: Provides authoritative guidance in their field and works under broad direction. Accountable for delivering significant work outcomes, from analysis through execution to evaluation.

Designs, implements, tests and improves machine learning architectures and systems.

Selects techniques based on a breadth of knowledge of the strengths, weaknesses and expected performance of different approaches.

Establishes good practice in the development, evaluation, monitoring and deployment of machine learning systems.

Machine learning: Level 6

Level 6 - Initiate, influence: Essence of the level: Has significant organisational influence, makes high-level decisions, shapes policies, demonstrates leadership, promotes organisational collaboration, and accepts accountability in key areas.

Leads the development of new approaches and organisational capabilities to design, train, and evaluate machine learning systems.

Sets standards and guidelines for the application and traceability of machine learning systems to business problems, and oversees their implementation.

Designs and oversees organisational policies on the creation, training and use of machine learning systems.